Nuclear magnetic resonance logging data processing method and device, equipment and storage medium

Abstract

The invention provides a nuclear magnetic resonance logging data processing method, device and equipment and a storage medium, and belongs to the technical field of nuclear magnetic resonance logging. The method comprises the steps that in response to input of a saturated water T2 spectrum in nuclear magnetic resonance logging data, a built variable T2 cut-off value calculation model generates a T2 cut-off value corresponding to the current saturated water T2 spectrum, and the building process of the variable T2 cut-off value calculation model comprises the steps of building a Gaussian mixture function inversion algorithm, a characteristic parameter generation algorithm and a T2 cut-off value generation algorithm. Compared with a traditional fitting method, the method has the advantages that the use of an empirical formula is avoided, meanwhile, compared with a traditional morphological analysis method, the defects of large workload and high error caused by manual T2 spectrum classification are avoided, the calculation efficiency and the calculation precision of the T2 cutoff value are improved, and the calculation precision of later reservoir parameters is effectively supported.

Description

Technical Field

[0001] The present invention belongs to the technical field of nuclear magnetic resonance logging, especially for the exploration and development of oil and gas. Specifically, it relates to a method, device, equipment and storage medium for processing nuclear magnetic resonance logging data. Background Art

[0002] Nuclear magnetic resonance logging plays an important role in the exploration and development of complex oil and gas reservoirs because it can directly provide information on the pore size and its distribution in the reservoir. The data at each depth point of nuclear magnetic resonance logging data can be understood as a curve of T2 time and signal relative amplitude, which is called the nuclear magnetic resonance logging T2 distribution spectrum, abbreviated as T2 distribution or T2 spectrum. Generally, the T2 distribution can reflect the pore size distribution. Larger pore sizes correspond to larger T2 values, and smaller pore sizes correspond to smaller T2 values. The nuclear magnetic resonance T2 cut-off value is the boundary between movable fluid and bound fluid. In the T2 spectrum of a saturated sample, the part greater than the T2 cut-off value is movable fluid, and vice versa is bound fluid. Therefore, determining an accurate nuclear magnetic resonance T2 cut-off value is crucial for the later evaluation of reservoir effectiveness and fluid type identification.

[0003] Currently, the commonly used calculation methods for the T2 cut-off value mainly include the empirical value method based on a fixed cut-off value and the variable T2 cut-off value method. The variable T2 cut-off value method includes the fitting method and the morphological analysis method, etc. The fixed cut-off value selected by the empirical value method is often discovered by scientists through a large number of rock physics experiments. For example, the T2 cut-off value of carbonate rock samples is about 100 ms, and the T2 cut-off value of clastic rock samples is 33 ms. However, as the exploration and development field gradually moves towards complex reservoirs, existing research believes that the variation range of the T2 cut-off value of different samples is relatively large. Calculating with a fixed T2 cut-off value will result in relatively large errors in the calculated reservoir parameters. The idea of the fitting method is that some scholars have found through a large number of rock physics experiments that there is a certain relationship between the T2 geometric mean and the T2 cut-off value. By establishing a fitting relationship between the two through experimental data, continuous calculation of the T2 cut-off value can be achieved. Some scholars also believe that there is a certain relationship between conventional logging data such as longitudinal wave velocity and density and the T2 cut-off value. After establishing a fitting relationship between experimental data and logging data, continuous calculation of the T2 cut-off value can be achieved. Since the T2 cut-off value is affected by many factors such as porosity and pore structure, the accuracy of the T2 cut-off value calculated by fitting based on empirical formulas is often low. The morphological analysis method is to classify the T2 spectrum manually according to the morphological characteristics of the saturated T2 spectrum, and statistically analyze the relationship between the T2 cut-off value of different types and parameters such as the peak of the centrifugal T2 spectrum, so as to achieve the calculation of the T2 cut-off value. This method requires a large amount of data to be manually sorted and classified according to the morphology, with a huge workload, and the accuracy is easily affected by humans, resulting in low accuracy.

[0004] In summary, there is an urgent need to seek an improved method for processing nuclear magnetic resonance data to achieve efficient and high-precision calculation of the T2 cut-off value. Summary of the Invention

[0005] The object of the embodiments of the present invention is to provide a nuclear magnetic resonance logging data processing method, device, equipment and storage medium, so as to solve the technical problems in the prior art that the T2 cut-off value accuracy of the empirical value method and the fitting method is relatively low, and the traditional morphological analysis method has a large workload and relatively low accuracy.

[0006] To achieve the above object, the first aspect of the present invention provides a nuclear magnetic resonance logging data processing method, and the method includes:

[0007] In response to the input of the saturated water T2 spectrum in the nuclear magnetic resonance logging data, the constructed variable T2 cut-off value calculation model generates a T2 cut-off value corresponding to the current saturated water T2 spectrum;

[0008] The construction process of the variable T2 cut-off value calculation model is specifically as follows:

[0009] Construct a mixed Gaussian function inversion algorithm, which establishes an inversion objective function representing the error value between the experimental saturated water T2 spectrum and the mixed Gaussian function for fitting the experimental saturated water T2 spectrum, gradually increases the order of the mixed Gaussian function from 1, calculates the output value of the inversion objective function for each order, and takes the order corresponding to the minimum output value as the determined order of the mixed Gaussian function to obtain a mixed Gaussian function with a determined order;

[0010] Construct a characteristic parameter generation algorithm, which uses the mixed Gaussian function determined by the mixed Gaussian function inversion algorithm to fit the experimental saturated water T2 spectrum to obtain the characteristic parameters of the mixed Gaussian function corresponding to each logging depth;

[0011] Construct a T2 cut-off value generation algorithm with the characteristic parameters as the input, and the expression of the algorithm is obtained by performing multiple fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value;

[0012] Among them, the experimental saturated water T2 spectrum and the true T2 cut-off value corresponding to the depth are obtained by collecting and organizing nuclear magnetic resonance rock physics experimental data.

[0013] Optionally, the mixed Gaussian function inversion algorithm selects the least squares method to calculate the inversion objective function.

[0014] Optionally, the expression of the inversion objective function is

[0015] Among them, T2(x) is the experimental saturated water T2 spectrum, f(x) is the mixed Gaussian function and n is the order of the mixed Gaussian function, x is the T2 time, αi is the amplitude value of the Gaussian function numbered i, μ i is the mathematical expectation value of the Gaussian function numbered i, σ i is the variance of the Gaussian function numbered i, i = 1, 2,..., n.

[0016] Optionally, when the order of the mixture Gaussian function gradually increases starting from 1, the order of the mixture Gaussian function takes values 1, 2, and 3 in sequence.

[0017] Optionally, the characteristic parameters include the amplitude value, mathematical expectation value, and variance of each Gaussian function within the mixture Gaussian function.

[0018] Optionally, the method further includes: analyzing nuclear magnetic resonance logging data by using the T2 cut-off value generated by the variable T2 cut-off value calculation model to obtain reservoir parameters of the logging work area.

[0019] The second aspect of the present invention provides a nuclear magnetic resonance logging data processing device, which loads a variable T2 cut-off value calculation model, and the variable T2 cut-off value calculation model is used to generate a T2 cut-off value corresponding to the current saturated water T2 spectrum in response to the input of the saturated water T2 spectrum in the nuclear magnetic resonance logging data;

[0020] The construction process of the variable T2 cut-off value calculation model is specifically as follows:

[0021] Construct a mixture Gaussian function inversion algorithm, which establishes an inversion objective function representing the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function for fitting the experimental saturated water T2 spectrum, gradually increases the order of the mixture Gaussian function starting from 1, calculates the output value of the inversion objective function for each order, and takes the order corresponding to the minimum output value as the determined order of the mixture Gaussian function to obtain a mixture Gaussian function with a determined order;

[0022] Construct a characteristic parameter generation algorithm, which uses the mixture Gaussian function determined by the mixture Gaussian function inversion algorithm to fit the experimental saturated water T2 spectrum to obtain the characteristic parameters of the mixture Gaussian function corresponding to each logging depth;

[0023] Construct a T2 cut-off value generation algorithm with the characteristic parameters as the input, and the expression of the algorithm is obtained by performing multiple fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value;

[0024] Among them, the experimental saturated water T2 spectrum and the true T2 cut-off value corresponding to the depth are obtained by collecting and sorting out nuclear magnetic resonance rock physics experimental data.

[0025] Optionally, the mixture Gaussian function inversion algorithm selects the least squares method to calculate the inversion objective function.

[0026] Optionally, the expression of the inversion objective function is

[0027] where T2(x) is the experimental saturated water T2 spectrum, f(x) is a mixture Gaussian function and n is the order of the mixture Gaussian function, x is the T2 time, α i is the amplitude value of the Gaussian function numbered i, μ i is the mathematical expectation of the Gaussian function numbered i, σ i is the variance of the Gaussian function numbered i, and i = 1, 2, ..., n.

[0028] Optionally, when the order of the mixture Gaussian function gradually increases starting from 1, the order of the mixture Gaussian function takes values 1, 2, and 3 in sequence.

[0029] Optionally, the characteristic parameters include the amplitude value, mathematical expectation, and variance of each Gaussian function within the mixture Gaussian function.

[0030] Optionally, the device further includes a reservoir parameter calculation module, which is configured to analyze nuclear magnetic resonance logging data by using the T2 cut-off value generated by the variable T2 cut-off value calculation model to obtain the reservoir parameters of the logging work area.

[0031] A third aspect of the present invention provides a device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the nuclear magnetic resonance logging data processing method as described in the first aspect of the present invention is implemented.

[0032] A fourth aspect of the present invention provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the nuclear magnetic resonance logging data processing method as described in the first aspect of the present invention is implemented.

[0033] Through the above technical solution, based on nuclear magnetic resonance (NMR) rock physics experiments, the present application utilizes the characteristic that the T2 spectrum satisfies the Gaussian distribution to construct a variable T2 cut-off value calculation model. This model obtains a mixed Gaussian function of a determined order through an inversion method such as the least squares method, and uses the mixed Gaussian function of the determined order to fit the NMR saturated water T2 spectrum at each logging depth, thereby obtaining the characteristic parameters of the mixed Gaussian function corresponding to each logging depth itself. Then, a multiple fitting relationship is established using the characteristic parameters and the T2 cut-off value determined in the laboratory (also known as the true T2 cut-off value). After obtaining the current actual NMR logging data, rapid and highly accurate continuous calculation of the T2 cut-off value can be achieved, effectively supporting the analysis and calculation of reservoir parameters in the logging work area. The calculated reservoir parameters such as movable porosity, irreducible porosity, and irreducible water saturation have high accuracy, and the calculation efficiency is also improved.

[0034] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used together with the following specific implementation to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0036] Figure 1 is the first flow schematic diagram of the NMR logging data processing method provided by the present invention;

[0037] Figure 2 is a schematic diagram of a construction process of a variable T2 cut-off value calculation model;

[0038] Figure 3 is the second flow schematic diagram of the NMR logging data processing method provided by the present invention;

[0039] Figure 4 is a schematic diagram of the fitting relationship between the mixed Gaussian function and the saturated water T2 spectrum;

[0040] Figure 5 is a schematic diagram of the result of multiple fitting based on the characteristic parameters of the mixed Gaussian function and the laboratory T2 cut-off value;

[0041] Figure 6 is an application result schematic diagram of the NMR logging data processing method provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following is a detailed description of the specific implementation manners of the embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts fall within the scope of protection of this application.

[0043] Method Embodiment

[0044] Please refer to Figure 1 , the present invention provides a method for processing nuclear magnetic resonance logging data, and the method specifically includes the following implementation steps:

[0045] S100. Input the saturated water T2 spectrum in the actual nuclear magnetic resonance logging data into the constructed variable T2 cut-off value calculation model.

[0046] S200. The variable T2 cut-off value calculation model generates the corresponding T2 cut-off value according to the input current saturated water T2 spectrum.

[0047] Among them, as shown in combination with Figure 2 , the specific construction steps of the variable T2 cut-off value calculation model are as follows:

[0048] S01. Based on nuclear magnetic resonance rock physics experiments, after obtaining the nuclear magnetic resonance rock physics experiment data of the logging work area, collect and organize the experimental saturated water T2 spectrum and the T2 cut-off value corresponding to the depth of each experimental saturated water T2 spectrum. In the following content, the T2 cut-off value corresponding to each collected and organized depth is called the true T2 cut-off value.

[0049] S02. Construct a mixed Gaussian function inversion algorithm. This algorithm establishes an inversion objective function that characterizes the error value between the experimental saturated water T2 spectrum and the mixed Gaussian function for fitting the experimental saturated water T2 spectrum. Gradually increase the order of the mixed Gaussian function from 1, calculate the output value of the inversion objective function for each order, and take the order corresponding to the minimum output value as the determined order of the mixed Gaussian function to obtain a mixed Gaussian function with a determined order. The order of the mixed Gaussian function represents the number of Gaussian functions in the mixed Gaussian function.

[0050] Specifically, the expression of the mixed Gaussian function in S02 is as shown in Equation (1):

[0051]

[0052] Among them, n represents the order of the mixed Gaussian function, x represents the nuclear magnetic resonance T2 time, α i represents the amplitude value of the Gaussian function numbered i, μ i represents the mathematical expectation of the Gaussian function numbered i, σ iIt represents the variance of the Gaussian function numbered i, where i = 1, 2,..., n.

[0053] Furthermore, the expression of the inversion objective function is as shown in Equation 2:

[0054]

[0055] Exemplarily, in one embodiment:

[0056] S02. Construct a mixture Gaussian function inversion algorithm. This algorithm establishes an inversion objective function that characterizes the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function for fitting the experimental saturated water T2 spectrum. It gradually increases the order of the mixture Gaussian function from 1, calculates the output value of the inversion objective function for each order, takes the order corresponding to the minimum output value as the determined order of the mixture Gaussian function, and obtains a mixture Gaussian function with a determined order. The order of the mixture Gaussian function represents the number of Gaussian functions in the mixture Gaussian function;

[0057] Among them, when determining the final order of the mixture Gaussian function, the experimental saturated water T2 spectra of different rock physics experimental samples are discussed separately. Specifically, it means that for the experimental saturated water T2 spectrum of each rock physics experimental sample, a mixture Gaussian function inversion is performed to determine the order of the mixture Gaussian function that makes the inversion objective function obtain the minimum output value, that is, to determine the value of n. By using different rock physics experimental samples in S02, compared with a single sample, the accuracy of the determined value of n is improved.

[0058] Exemplarily, in another embodiment:

[0059] S02. Construct a mixture Gaussian function inversion algorithm. This algorithm establishes an inversion objective function that characterizes the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function for fitting the experimental saturated water T2 spectrum. It gradually increases the order of the mixture Gaussian function from 1, calculates the output value of the inversion objective function for each order, takes the order corresponding to the minimum output value as the determined order of the mixture Gaussian function, and obtains a mixture Gaussian function with a determined order. The order of the mixture Gaussian function represents the number of Gaussian functions in the mixture Gaussian function;

[0060] Among them, when gradually increasing the order of the mixture Gaussian function from 1, it is preferable to sequentially take the values 1, 2, and 3 for the order of the mixture Gaussian function.

[0061] Exemplarily, in another embodiment:

[0062] S02. Construct a mixed Gaussian function inversion algorithm. This algorithm establishes an inversion objective function that represents the error value between the experimental saturated water T2 spectrum and the mixed Gaussian function used to fit the experimental saturated water T2 spectrum. The order of the mixed Gaussian function is gradually increased starting from 1. For each order, the output value of the inversion objective function is calculated, and the order corresponding to the minimum output value is taken as the determined order of the mixed Gaussian function, obtaining a mixed Gaussian function with a determined order. The order of the mixed Gaussian function represents the number of Gaussian functions in the mixed Gaussian function.

[0063] Among them, the mixed Gaussian function inversion algorithm selects the least squares method to calculate the inversion objective function. The specific calculation process is performed based on the least squares principle in the ordinary embodiment, and this embodiment does not describe this part of the content in detail.

[0064] S03. Construct a characteristic parameter generation algorithm. The characteristic parameter generation algorithm is used to fit the experimental saturated water T2 spectrum with the mixed Gaussian function determined by the mixed Gaussian function inversion algorithm to obtain the characteristic parameters of the mixed Gaussian function corresponding to each logging depth.

[0065] It is known that the self-parameters of the Gaussian function generally include the mathematical expectation, variance, etc. The self-parameters of the mixed Gaussian function also include the amplitude values of each Gaussian function, etc.

[0066] Exemplarily, in one embodiment:

[0067] S03. Construct a characteristic parameter generation algorithm. The characteristic parameter generation algorithm is used to fit the experimental saturated water T2 spectrum with the mixed Gaussian function determined by the mixed Gaussian function inversion algorithm to obtain the characteristic parameters of the mixed Gaussian function corresponding to each logging depth;

[0068] Among them, the characteristic parameters of the mixed Gaussian function include the amplitude values, mathematical expectations, and variances of each Gaussian function within the mixed Gaussian function.

[0069] S04. Construct a T2 cut-off value generation algorithm that takes the characteristic parameters as input. The expression of the T2 cut-off value generation algorithm is obtained by performing multiple fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value.

[0070] It should be understood that multivariate fitting is to obtain the correlation between the multi-dimensional characteristic parameters of the mixture Gaussian function and the T2 cut-off value through regression. The regression method can adopt the regression analysis process in the ordinary embodiment, and this embodiment will not describe this part of the content in detail. For example, when the value of n in the mixture Gaussian function is 2, the amplitude value of the first Gaussian function in the mixture Gaussian function is denoted as α1, its mathematical expectation is denoted as μ1, and its variance is denoted as σ1. The amplitude value of the second Gaussian function in the mixture Gaussian function is denoted as α2, its mathematical expectation is denoted as μ2, and its variance is denoted as σ2. Therefore, the characteristic parameters input into the T2 cut-off value generation algorithm include α1, μ1, σ1, α2, μ2, and σ2. Correspondingly, if the multivariate fitting relationship obtained through regression analysis is a linear equation with multiple variables, the multivariate fitting relationship can be expressed as Equation 3:

[0071] T 2cutoff = k0 + k1*α1 + k2*μ1 + k3*σ1 + k4*α2 + k5*μ2 + k6*σ2 (Equation 3);

[0072] Where, T 2cutoff represents the T2 cut-off value.

[0073] In some existing technologies, for example, in the patent publication document with the application number CN201811203489.7, which utilizes the principle that there is a correlation between the T2 geometric mean and the T2 cut-off value, for the nuclear magnetic resonance experimental data of rock samples in the target work area, the geometric mean Gc and the pseudo-geometric mean G' of the cut-off time of the nuclear magnetic resonance T2 spectrum are calculated respectively. After constructing the Gc, G' data matrix, the pseudo T2 cut-off value T2'c of the target work area is determined by the least squares algorithm. Then, the pseudo cut-off time geometric mean G'c of the nuclear magnetic resonance T2 spectrum is calculated from T2'c, and the fitting relationship between the T2 cut-off value T2cutoff and G'c is determined. Then, the G'c of the measured nuclear magnetic resonance logging data in the target work area is calculated, and the fitting relationship determined above is used to realize the point-by-point calculation of the nuclear magnetic resonance logging T2 cut-off value. A relatively large number of empirical formulas are adopted in this technical solution. However, the T2 cut-off value is often affected by various factors such as porosity and pore structure, and the empirical formulas are not applicable to all logging work areas, resulting in a low accuracy of the T2 cut-off value obtained by the above technical disclosure.

[0074] Furthermore, for example, the patent publication with the application number CN200510072232.9 relates to a method for determining the T2 cut-off value of the T2 spectrum in nuclear magnetic resonance logging, including the following steps: classifying the nuclear magnetic resonance logging T2 spectrum according to its morphological characteristics; fitting the centrifugal spectrum based on different types of logging T2 spectra; performing area integration on the centrifugal spectrum to obtain the area of the centrifugal spectrum; calculating the area of the logging T2 spectrum; accumulating the area of the logging T2 spectrum, and when the accumulated area is equal to or closest to the area of the centrifugal spectrum, the corresponding T2 value is the T2 cut-off value. In this technical solution, the nuclear magnetic resonance T2 spectrum is manually distinguished as single-peak type, double-peak type, or triple-peak type, and many empirical formulas are also used. However, the empirical formulas are not applicable to all logging work areas, resulting in a low accuracy of the T2 cut-off value obtained from the above technical disclosure and a low calculation efficiency.

[0075] In this embodiment, based on the nuclear magnetic resonance rock physics experiments in the research area, the experimentally obtained saturated water T2 spectrum and the T2 cut-off value at the corresponding depth are used as the data sources for constructing the variable T2 cut-off value calculation model. First, the mixed Gaussian function is inverted by the inversion method based on the least squares method to determine the n value, and then the mixed Gaussian function is used to fit the saturated water T2 spectrum to determine the characteristic parameters of the mixed Gaussian function. Finally, based on regression analysis, a relationship between the characteristic parameters of the mixed Gaussian function and the experimental T2 cut-off value is established, thereby realizing the continuous calculation of the nuclear magnetic resonance logging T2 cut-off value. This method does not use empirical formulas, and different data sources are used for different research areas to construct the variable T2 cut-off value calculation model. Through the demonstration of multiple research areas, it is found that the calculated T2 cut-off value is in good agreement with the experimentally analyzed T2 cut-off value. The error between the movable porosity obtained by this method and the experimentally analyzed movable porosity is within 8%, which proves the effectiveness of the nuclear magnetic resonance logging data processing method implemented in this embodiment, and is compatible with high efficiency and high precision.

[0076] Further, please refer to Figure 3 , a nuclear magnetic resonance logging data processing method provided by the present invention further includes the following implementation steps:

[0077] S300. Using the T2 cut-off value generated by the variable T2 cut-off value calculation model to analyze the nuclear magnetic resonance logging data to obtain the reservoir parameters of the logging work area. It is known that the reservoir parameters include movable porosity, bound porosity, bound water saturation, etc.

[0078] Please refer to Figures 4 to 6 , Figures 4 to 6 reflects a specific application example of the above method embodiment in a specific research area. Specifically, the specific research area is the Dibe area. Correspondingly, a specific implementation process of the nuclear magnetic resonance logging data processing method is as follows:

[0079] Step A1. Organize the nuclear magnetic resonance rock physics experiments in the Dibe area, and collect the experimental saturated water T2 spectrum data and the true T2 cut-off value corresponding to the depth.

[0080] Step A2. Select the experimental saturated water T2 spectrum data of a well in the Dibe area, and use the least squares method to invert the saturated water T2 spectrum. The obtained mixed Gaussian function is as shown in Equation (4):

[0081]

[0082] Specifically, for the inversion results, refer to Figure 4 , in Figure 4 , the abscissa is the T2 time, with the unit of ms, the ordinate is the porosity component data at different times, the dot data is the original experimental saturated water T2 spectrum data, and the reconstructed mixed Gaussian function curve obtained by inversion is composed of two Gaussian functions. The first Gaussian function is the one with the peak on the left and the curve marked with a triangle, and the second Gaussian function is the one with the peak on the right and the curve marked with a circle. As can be seen from the figure, the T2 spectrum obtained by inversion is basically consistent with the experimental saturated water T2 spectrum, and the correlation coefficient is 0.994. Finally, the n value of the research area is determined to be 2.

[0083] Step A3. Use the mixed Gaussian function with the determined n value obtained in Step A2 to fit the experimental saturated water T2 spectrum experimental data at each depth, and obtain the characteristic parameters of the mixed Gaussian function corresponding to each depth.

[0084] Step A4. Use the characteristic parameters of the mixed Gaussian function corresponding to each depth obtained in Step A3 and the laboratory-determined T2 cut-off value (true T2 cut-off value) corresponding to the depth for regression analysis, and establish a multiple fitting relationship between the two, as shown in Equation (5):

[0085] T 2cutoff =-56.3 + 40.9*α1 - 10.8*μ1 + 35.5*σ1 + 68.1*α2 - 7.1*μ2 + 11.1*σ2 (Equation 5);

[0086] Among them, α1 above is the amplitude value of the first Gaussian function, μ1 is the mathematical expectation of the first Gaussian function, σ1 is the variance of the first Gaussian function, α2 is the amplitude value of the second Gaussian function, μ2 is the mathematical expectation of the second Gaussian function, and σ2 is the variance of the second Gaussian function.

[0087] Specifically, for the fitting results, refer to Figure 5 shown. As can be seen from the figure, the T2 cut-off value calculated using Equation (5) is basically consistent with the laboratory-determined T2 cut-off value.

[0088] Step A5. Compile the variable T2 cut-off value calculation model constructed through Steps A1 - A4, and attach it to a hardware carrier such as a logging data analysis platform to run and implement the method implemented in this embodiment. After performing nuclear magnetic resonance logging on each well in the study area, actual nuclear magnetic resonance logging data is obtained. Input the above data into the variable T2 cut-off value calculation model to obtain the corresponding T2 cut-off value, and use this T2 cut-off value to calculate reservoir parameters, such as movable porosity, total porosity, and irreducible porosity, etc. The logging data analysis platform can be the CIFLOG platform, etc.;

[0089] Specifically, Figure 6 The calculation result diagrams of some reservoir parameters are shown. Among them, the sixth track is the comparison track of the T2 cut-off value generated by the variable T2 cut-off value calculation model and the T2 cut-off value obtained through experimental analysis. It can be seen from the figure that the T2 cut-off value generated by the variable T2 cut-off value calculation model coincides with the T2 cut-off value obtained through experimental analysis; the seventh track is the comparison track of the movable porosity calculated using the generated T2 cut-off value and the movable porosity obtained through experimental analysis. It can be seen from the figure that the movable porosity calculated using the generated T2 cut-off value is relatively consistent with the movable porosity obtained through experimental analysis; the eighth track is the comparison track of the total porosity calculated using the generated T2 cut-off value and the total porosity obtained through experimental analysis. It can be seen from the figure that the total porosity calculated using the generated T2 cut-off value is also relatively consistent with the total porosity obtained through experimental analysis.

[0090] Device Embodiment

[0091] The present invention provides a nuclear magnetic resonance logging data processing device loaded with a variable T2 cut-off value calculation model. The variable T2 cut-off value calculation model is used to generate a T2 cut-off value corresponding to the current saturated water T2 spectrum in response to the input of the saturated water T2 spectrum in the nuclear magnetic resonance logging data;

[0092] The construction process of the variable T2 cut-off value calculation model is specifically as follows:

[0093] Construct a mixture Gaussian function inversion algorithm. This algorithm establishes an inversion objective function that characterizes the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function for fitting the experimental saturated water T2 spectrum. Gradually increase the order of the mixture Gaussian function from 1, calculate the output value of the inversion objective function for each order, and take the order corresponding to the minimum output value as the determined order of the mixture Gaussian function to obtain a mixture Gaussian function with a determined order;

[0094] Construct a characteristic parameter generation algorithm. This algorithm uses the mixture Gaussian function determined by the mixture Gaussian function inversion algorithm to fit the experimental saturated water T2 spectrum to obtain the characteristic parameters of the mixture Gaussian function corresponding to each logging depth;

[0095] Construct a T2 cut-off value generation algorithm that takes characteristic parameters as inputs. The expression of this algorithm is obtained by performing multiple regression fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value.

[0096] Among them, the experimental saturated water T2 spectrum and the true T2 cut-off value at the corresponding depth are obtained by collecting and collating nuclear magnetic resonance rock physics experimental data.

[0097] Optionally, the mixed Gaussian function inversion algorithm selects the least squares method to calculate the inversion objective function.

[0098] Optionally, the expression of the inversion objective function is

[0099] Among them, T2(x) is the experimental saturated water T2 spectrum, f(x) is the mixed Gaussian function and n is the order of the mixed Gaussian function, x is the T2 time, α i is the amplitude value of the Gaussian function numbered i, μ i is the mathematical expectation of the Gaussian function numbered i, σ i is the variance of the Gaussian function numbered i, i = 1, 2,..., n.

[0100] Optionally, when the order of the mixed Gaussian function gradually increases starting from 1, the order of the mixed Gaussian function takes the values 1, 2, and 3 in sequence.

[0101] Optionally, the characteristic parameters include the amplitude value, mathematical expectation, and variance of each Gaussian function within the mixed Gaussian function.

[0102] In one embodiment, the nuclear magnetic resonance logging data processing device further includes a reservoir parameter calculation module. The reservoir parameter calculation module is used to analyze the nuclear magnetic resonance logging data by using the T2 cut-off value generated by the variable T2 cut-off value calculation model to obtain the reservoir parameters of the logging work area.

[0103] On the other hand, the present invention also provides a device. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the nuclear magnetic resonance logging data processing method in the above method embodiments.

[0104] The above variable T2 cut-off value calculation model and reservoir parameter calculation module are both stored in the memory as program units, and the corresponding functions are implemented by the processor executing the above program units stored in the memory. The processor includes a kernel, and the kernel retrieves the corresponding program units from the memory.

[0105] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0106] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0107] In another aspect, the present invention also provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the nuclear magnetic resonance logging data processing method in the above method embodiments.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0109] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical discs, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing nuclear magnetic resonance logging data, characterized in that, The method includes: In response to the input of the saturated water T2 spectrum in nuclear magnetic resonance logging data, the constructed variable T2 cut-off value calculation model generates a T2 cut-off value corresponding to the current saturated water T2 spectrum; The construction process of the variable T2 cut-off value calculation model is specifically as follows: Construct a mixture Gaussian function inversion algorithm, which establishes an inversion objective function representing the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function that fits the experimental saturated water T2 spectrum. Gradually increase the order of the mixture Gaussian function from 1. For each order, calculate the output value of the inversion objective function, and take the order corresponding to the minimum output value as the determined order of the mixture Gaussian function to obtain a mixture Gaussian function with a determined order; Construct a characteristic parameter generation algorithm, which uses the mixture Gaussian function determined by the mixture Gaussian function inversion algorithm to fit the experimental saturated water T2 spectrum to obtain the characteristic parameters of the mixture Gaussian function corresponding to each logging depth; Construct a T2 cut-off value generation algorithm with the characteristic parameters as the input. The expression of this algorithm is obtained by performing multiple regression fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value; Among them, the experimental saturated water T2 spectrum and the true T2 cut-off value corresponding to the depth are obtained by collecting and organizing nuclear magnetic resonance rock physics experimental data.

2. The method for processing nuclear magnetic resonance logging data according to claim 1, characterized in that, The mixture Gaussian function inversion algorithm selects the least squares method to calculate the inversion objective function.

3. The method for processing nuclear magnetic resonance logging data according to claim 1, characterized in that, The expression of the inversion objective function is Among them, T2(x) is the experimental saturated water T2 spectrum, f(x) is the mixed Gaussian function, and n is the order of the mixed Gaussian function, x is the T2 time, α i is the amplitude value of the Gaussian function numbered i, μ i is the mathematical expectation of the Gaussian function numbered i, σ i is the variance of the Gaussian function numbered i, and i = 1, 2,..., n.

4. The method for processing nuclear magnetic resonance logging data according to claim 1, characterized in that, When the order of the mixture Gaussian function is gradually increased from 1, the orders of the mixture Gaussian function are successively 1, 2, and 3.

5. The method for processing nuclear magnetic resonance logging data according to claim 1, characterized in that, The characteristic parameters include the amplitude value, mathematical expectation, and variance of each Gaussian function within the mixture Gaussian function.

6. The method for processing nuclear magnetic resonance logging data according to claim 1, characterized in that, The method further includes: Using the T2 cut-off value generated by the variable T2 cut-off value calculation model to analyze the nuclear magnetic resonance logging data to obtain the reservoir parameters of the logging work area.

7. A device for processing nuclear magnetic resonance logging data, characterized in that, The device is loaded with a variable T2 cut-off value calculation model, which is used to generate a T2 cut-off value corresponding to the current saturated water T2 spectrum in response to the input of the saturated water T2 spectrum in nuclear magnetic resonance logging data; The construction process of the variable T2 cut-off value calculation model is specifically as follows: Construct a mixture Gaussian function inversion algorithm, which establishes an inversion objective function representing the error value between the experimental saturated water T2 spectrum and the mixture Gaussian function that fits the experimental saturated water T2 spectrum. Gradually increase the order of the mixture Gaussian function from 1. For each order, calculate the output value of the inversion objective function, and take the order corresponding to the minimum output value as the determined order of the mixture Gaussian function to obtain a mixture Gaussian function with a determined order; Construct a characteristic parameter generation algorithm, which uses the mixture Gaussian function determined by the mixture Gaussian function inversion algorithm to fit the experimental saturated water T2 spectrum to obtain the characteristic parameters of the mixture Gaussian function corresponding to each logging depth; Construct a T2 cut-off value generation algorithm with the characteristic parameters as the input. The expression of this algorithm is obtained by performing multiple regression fitting on the characteristic parameters corresponding to each logging depth and the true T2 cut-off value; Among them, the experimental saturated water T2 spectrum and the true T2 cut-off value corresponding to the depth are obtained by collecting and organizing nuclear magnetic resonance rock physics experimental data.

8. The device for processing nuclear magnetic resonance logging data according to claim 7, characterized in that, The inversion algorithm of the mixture Gaussian function selects the least squares method to calculate the inversion objective function.

9. The device for processing nuclear magnetic resonance logging data according to claim 7, characterized in that, The expression of the inversion objective function is Among them, T2(x) is the experimental saturated water T2 spectrum, f(x) is the mixed Gaussian function, and n is the order of the mixed Gaussian function, x is the T2 time, α i is the amplitude value of the Gaussian function numbered i, μ i is the mathematical expectation of the Gaussian function numbered i, σ i is the variance of the Gaussian function numbered i, and i = 1, 2,..., n.

10. The device for processing nuclear magnetic resonance logging data according to claim 7, characterized in that, When the order of the mixture Gaussian function gradually increases starting from 1, the order of the mixture Gaussian function takes values 1, 2, and 3 in sequence.

11. The device for processing nuclear magnetic resonance logging data according to claim 7, characterized in that, The characteristic parameters include the amplitude value, mathematical expectation, and variance of each Gaussian function within the mixture Gaussian function.

12. The nuclear magnetic resonance logging data processing device according to claim 7, characterized in that The device further includes a reservoir parameter calculation module, which is used to analyze the nuclear magnetic resonance logging data by using the T2 cut-off value generated by the variable T2 cut-off value calculation model to obtain the reservoir parameters of the logging work area.

13. A device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the program, it implements the nuclear magnetic resonance logging data processing method according to any one of claims 1 to 6.

14. A storage medium, on which a computer program is stored, characterized in that When the computer program is executed by the processor, it implements the nuclear magnetic resonance logging data processing method according to any one of claims 1 to 6.

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